diff --git a/app.py b/app.py
index e45a2d1..fd478bc 100644
--- a/app.py
+++ b/app.py
@@ -12,7 +12,8 @@ from langchain.document_loaders.recursive_url_loader import RecursiveUrlLoader
 from langchain.document_loaders import UnstructuredURLLoader
 from langchain.document_loaders.csv_loader import CSVLoader
 from langchain import  LLMChain
-from langchain.llms import GPT4All
+#from langchain.llms import GPT4All
+from gpt4all import GPT4All
 from langchain.embeddings import GPT4AllEmbeddings
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain.callbacks.base import BaseCallbackManager
@@ -26,9 +27,9 @@ import time
 import re
 
 def loadModels():
-    #model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
-    callback_manager = BaseCallbackManager([StreamingStdOutCallbackHandler()])
-    llm = GPT4All(model="orca-mini-3b.ggmlv3.q4_0.bin",temp=0.1,streaming=True,callback_manager=callback_manager,)# verbose=True,repeat_last_n=0
+    llm = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
+
+    #llm = GPT4All(model="orca-mini-3b.ggmlv3.q4_0.bin",temp=0.1,streaming=True,callbacks=[StreamingStdOutCallbackHandler()])# verbose=True,repeat_last_n=0
     embeddings = GPT4AllEmbeddings()
     return llm, embeddings
 
@@ -38,11 +39,14 @@ st2=time.time()
 
 Archives=[["https://www.gob.mx/sectur","Web"],
           ["https://centrohistorico.pueblacapital.gob.mx/nuestro-centro-historico/nuestro-blog/item/33-capilla-de-la-virgen-del-rosario","Web"],
-          ["https://capilladelrosariopuebla.com.mx/","Web"],
+          #["https://capilladelrosariopuebla.com.mx/","Web"],
          #["https://www.tripadvisor.es/Tourism-g150768-Mexico-Vacations.html","Web"],
           ["https://www.mexicodestinos.com/blog/destinos-mexico/","Web"],
          ["https://visitmexico.com/","Web"],
           ["https://www.turismomexico.es/","Web"],
+          ["https://es.wikipedia.org/wiki/Capilla_del_Rosario_(Puebla)","Web"],
+          ["https://www.mexicodesconocido.com.mx/capilla-del-rosario-puebla.html","Web"]
+
           
          ]
 
@@ -71,9 +75,9 @@ A=makeDb(Archives)
 
 def makeFinder():
     text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size = 1000,
+        chunk_size = 250,
         length_function=len,
-        chunk_overlap=200
+        chunk_overlap=50
     )
     documents = text_splitter.split_documents(A)
     try:
@@ -106,6 +110,23 @@ Question:
 
 Respuesta:"""
 
+# prompt_template = f"""
+# ### System:
+# Reponde la pregunta basado en el Contexto dado.
+# Si la pregunta no puede ser contestada usando la informacion dada
+# responder con "No poseo conocimiento sobre ese tema". Responder siempre en español.
+
+# ### User:
+
+# Contexto:
+# {{relevant_context}}
+
+# Pregunta:
+# {{user_query}}
+
+# ### Response:
+
+# Respuesta:"""
 
 prompt_template = PromptTemplate(
     input_variables=["relevant_context","user_query"], template=prompt_template
@@ -116,11 +137,11 @@ prompt_template = PromptTemplate(
 #         relevant_context="Otaisa es el lugar mas lindo de mexico pero esta muy cerca de nethai que no es tan lindo pero la comida es muy buena"
 #     )
 # print(prompt)
-llm_chain = LLMChain(llm=llm,prompt=prompt_template)
+#llm_chain = LLMChain(llm=llm,prompt=prompt_template)
 
 
 def FinderDb(query,dbs,filtred=False):
-    Sal = dbs.similarity_search_with_score(query,2)
+    Sal = dbs.similarity_search_with_score(query,9)
     page_content=[]
     d=[]
     if filtred:
@@ -128,7 +149,6 @@ def FinderDb(query,dbs,filtred=False):
     else:
         lim=9000000
     for output in Sal:
-        print(output)
         if output[1]<lim:
             page_content.append(output[0].page_content)
             d.append(output[1])
@@ -138,21 +158,37 @@ def FinderDb(query,dbs,filtred=False):
     return "/n".join(page_content),d
 
 
-def QARequest(Pregunta,filtred=False):
-    query = Pregunta
+def QARequest(message, history):
+    text="""Hola este chatbot esta en un periodo de prueba, si encuentras alguna falla Notificala y muchas gracias por usarlo.
+Respecto a tu pregunta la respuesta es:    """
+    query = message
     context,d=FinderDb(query,db,filtred=True)
-    #A=llm_chain.run(user_query=query,relevant_context=context)
-    return context,d#A
+    prompt=prompt_template.format(
+        user_query=query,
+        relevant_context=context
+    )
+    print("#############",prompt,"#############",)
+    text2=""
+    for i in text:
+        time.sleep(0.7)
+        text2=text2+i
+        yield text2
+    for i in llm.generate(prompt, max_tokens=200, streaming=True,temp=0.01, top_k=40, top_p=0.4):
+        time.sleep(0.7)
+        text2=text2+i
+        yield text2
 
 
 #question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
-with gr.Blocks() as demo:
-    Pregunta = gr.Textbox(label="Pregunta")
-    Respuesta = gr.Textbox(label="Respuesta")
-    metrica=gr.Textbox(label="metrica")
-    Enviar_btn = gr.Button("Responder")
-    Enviar_btn.click(fn=QARequest, inputs=[Pregunta], outputs=[Respuesta,metrica], api_name="Angela") 
+# with gr.Blocks() as demo:
+#     Pregunta = gr.Textbox(label="Pregunta")
+#     Respuesta = gr.Textbox(label="Respuesta")
+#     metrica=gr.Textbox(label="metrica")
+#     Enviar_btn = gr.Button("Responder")
+#     Enviar_btn.click(fn=QARequest, inputs=[Pregunta], outputs=[Respuesta], api_name="Angela") 
+gr.ChatInterface(QARequest).queue().launch()
 
-demo.launch() #  
+
+#demo.queue().launch() #  
 #print(A,time.time()-st,time.time()-st2)
 
diff --git a/app2.py b/app2.py
new file mode 100644
index 0000000..1e6b110
--- /dev/null
+++ b/app2.py
@@ -0,0 +1,68 @@
+import time
+import gradio as gr
+from langchain.llms import GPT4All
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from gpt4all import GPT4All
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+# Callbacks support token-wise streaming
+model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
+
+template = """Question: {question}
+
+Answer: Let's think step by step."""
+
+prompt = PromptTemplate(template=template, input_variables=["question"])
+
+prompt_template = """
+### System:
+Answer the question based on the input below. 
+If the question cannot be answered using the information 
+provided answer with "I don't know". Ever answer in spanish.
+
+### Input:
+%s
+
+### User:
+Question:
+%s
+
+
+
+
+### Response:
+Respuesta:"""
+
+
+
+
+def FinderDb(query,dbs,filtred=False):
+    Sal = dbs.similarity_search_with_score(query,9)
+    page_content=[]
+    d=[]
+    if filtred:
+        lim=1.5
+    else:
+        lim=9000000
+    for output in Sal:
+        if output[1]<lim:
+            page_content.append(output[0].page_content)
+            d.append(output[1])
+    if len(page_content)<1:
+        return None,None
+    
+    return "/n".join(page_content),d
+
+def slow_echo(message, history):
+    text=""
+    context,d=FinderDb(query,db,filtred=True)
+    query=prompt_template%("Otaisa es la cuidad mas linda de parita",message)
+    print(query)
+    for i in model.generate(query, max_tokens=200, streaming=True,temp=0.1, top_k=40, top_p=0.4):
+        time.sleep(0.03)
+        text=text+i
+        yield text
+
+gr.ChatInterface(slow_echo).queue().launch()
+